Adult Census Income Dataset

The following was retrieved from UCI machine learning repository. This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)). The prediction task is to determine whether a person makes over $50K a year. Description of fnlwgt (final weight)… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/adult-census-income.

This dataset has been taken from the famous UCI Machine Learning Repository. The goal of this notebook is to accurately predict whether or not an adult makes more than 50000 US Dollars in an year on the basis of the features given. Age: Describes the age of individuals. Continuous. fnlwgt: Continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. education-num: Number of years spent in education. Continuous. sex: Female, Male. capital-gain: Continuous. capital-loss: Continuous. hours-per-week: Continuous. salary: >50K,<=50K

Adult

The Adult dataset from the UCI ML repository. Census dataset including personal characteristic of a person, and their income threshold.

  Configurations and tasks

Configuration Task Description

encoding

Encoding dictionary showing original values of encoded features.

income Binary classification Classify the person's income as over or under the threshold.

income-no race Binary classification As income, but the race feature is removed.

race Multiclass… See the full description on the dataset page: https://huggingface.co/datasets/mstz/adult.

This study was conducted in Vijayapura City and Ukkali village in Vijayapura District, Karnataka State, India. This dataset includes all adults in the original sample. In each household an adult woman and adult man were invited to participate. In 37 households, only one adult household member was available. The adult dataset consists of Individual characteristics, household characteristics, public distribution system, housing environment, food choice, and food frequency.

The files "adult_train.csv" and "adult_test.csv" contain preprocessed versions of the Adult dataset from the USI repository.

The file "adult_preprocessing.ipynb" contains a python notebook file with all the preprocessing steps used to generate "adult_train.csv" and "adult_test.csv" from the original Adult dataset.

The preprocessing steps include:

One-hot-encoding of categorical values

Imputation of missing values using knn-imputer with k=1

Standard scaling of ordinal attributes

Note: we assume the scenario when the test set is available before training (every attribute besides the target - "income"), therefore we combine train and test sets before the preprocessing.

Analysis of ‘US Adult Income’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/johnolafenwa/us-census-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

US Adult Census data relating income to social factors such as Age, Education, race etc.

The Us Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. Each row is labelled as either having a salary greater than ">50K" or "<=50K".

This Data set is split into two CSV files, named adult-training.txt and adult-test.txt.

The goal here is to train a binary classifier on the training dataset to predict the column income_bracket which has two possible values ">50K" and "<=50K" and evaluate the accuracy of the classifier with the test dataset.

Note that the dataset is made up of categorical and continuous features. It also contains missing values The categorical columns are: workclass, education, marital_status, occupation, relationship, race, gender, native_country

The continuous columns are: age, education_num, capital_gain, capital_loss, hours_per_week

This Dataset was obtained from the UCI repository, it can be found on

https://archive.ics.uci.edu/ml/datasets/census+income, http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/

USAGE This dataset is well suited to developing and testing wide linear classifiers, deep neutral network classifiers and a combination of both. For more info on Combined Deep and Wide Model classifiers, refer to the Research Paper by Google https://arxiv.org/abs/1606.07792

Refer to this kernel for sample usage : https://www.kaggle.com/johnolafenwa/wage-prediction

Complete Tutorial is available from http://johnolafenwa.blogspot.com.ng/2017/07/machine-learning-tutorial-1-wage.html?m=1

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by PMR3508-2019-33

Contents

Dataset

This dataset was created by Rohit Amalnerkar

Contents

It contains the following files:

CHILD ADULT DATASET

## Overview

CHILD ADULT DATASET is a dataset for classification tasks - it contains CHILD ADULT annotations for 797 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Adult白天

## Overview

Adult白天 is a dataset for object detection tasks - it contains Adult1 annotations for 1,385 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Performance Dashboard (formerly Performance Outcomes System) datasets are developed to improve outcomes and inform beneficiaries who receive Medi-Cal Specialty Mental Health Services (SMHS). The intent of the dashboard is to gather information relevant to particular mental health outcomes, which will provide useful summary reports to help ensure ongoing quality improvement and to support decision making. Please note: the Excel file Performance Dashboard has been discontinued and replaced with the SMHS Performance Dashboards found on Behavioral Health Reporting (ca.gov).

Dataset

This dataset was created by PMR3508-2018-c8c305f210

Contents

Child And Adult

## Overview

Child And Adult is a dataset for object detection tasks - it contains Objects annotations for 273 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The National Reporting System (NRS) for Adult Education, 2017-18 (NRS 2017-18) is a performance accountability system for the national adult education program that is authorized under the Adult Education and Family Literacy Act (AEFLA), title II of the Workforce Innovation and Opportunity Act (WIOA) of 2014. More information about the program is available at . NRS 2017-18 is a cross-sectional data collection that is designed to monitor performance accountability for the federally funded, state-administered adult education program. States are required to submit their progress in adult education and literacy activities by reporting data on the WIOA primary indicators of performance for all AEFLA program participants who receive 12 or more hours of service, as well as state expenditures on the adult education program. States may also report on additional, optional secondary measures that include outcomes related to employment, family, and community. The data collection is conducted using a web-based reporting system. NRS 2017-18 is a universe data collection activity, and all states are required to submit performance data. Key statistics that are produced from the data collection include student demographics, receipt of secondary school diploma or a high school equivalency (HSE) credential, placement in postsecondary education or training, measurable skill gain, and employment outcomes.

huggingface/adult-census-competition dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Luis Hernandez

Contents

The Human Connectome Project (HCP) Young Adult dataset is used in this study. It is a diffusion-weighted imaging dataset collected using a multiband diffusion sequence on a 3T scanner.

The Metropolitan Police Department collects race and ethnicity data according to the United States Census Bureau standards (https://www.census.gov/topics/population/race/about.html). Hispanic, which was previously categorized under the Race field prior to August 2015, is now captured under Ethnicity. All records prior to August 2015 have been updated to “Unknown (Race), Hispanic (Ethnicity)”. Race, ethnicity and gender data are based on officer observation, which may or may not be accurate.MPD cannot release exact addresses to the general public unless proof of ownership or subpoena is submitted. The GeoX and GeoY values represent the block location (approximately 232 ft. radius) as of the date of the arrest and offense. Arrest and offense addresses that could not be geocoded are included as an “unknown” value.Arrestee age is calculated based on the number of days between the self-reported or verified date of birth (DOB) of the arrestee and the date of the arrest; DOB data may not be accurate if self-reported, and an arrestee may refuse to provide his or her date of birth. Due to the sensitive nature of juvenile data and to protect the arrestee’s confidentiality, any arrest records for defendants under the age of 18 or with missing age are excluded in this dataset.The Criminal Complaint Number (CCN) and arrest number have also been anonymized.This data may not match other arrest data requests that may have included all law enforcement agencies in the District or all arrest charges. Arrest totals are subject to change and may be different than MPD Annual Report totals or other publications due to inclusion of juvenile arrest summary, expungements, investigation updates, data quality audits, etc.

The National Reporting System for Adult Education, 2009-10 (NRS 2009-10), is part of the Adult Education and Family Literacy program; program data is available since 1997 at . NRS 2009-10 (http://www.nrsweb.org) is a cross-sectional study that was designed to monitor performance accountability for the federally funded, state-administered adult education program. States were required to submit their progress in adult education and literacy activities by reporting data on core indicators of outcomes on all adult learners who receive 12 or more hours of service as well as state expenditures on the adult education program. States could also report on additional, optional secondary measures that included outcomes related to employment, family, and community. The study was conducted using a web-based reporting system of states. NRS 2009-10 is a universe survey, and all states submitted data. Key statistics produced from the study include student demographics, reasons for attending the program, receipt of secondary school diploma or general education development (GED) certificate, placement in postsecondary education or training, educational gain, and employment placement and retention.

Those who experienced sexual violence as an adult by whether they ever disclosed a sexual violence experience (% of persons aged 18 years and over who experienced sexual violence as an adult)